Systems, apparatus, and methods for data integration optimization

ABSTRACT

Systems, methods, and techniques for optimizing a plurality of data integration tasks within a data integration collection by identifying, as a sub-set of the plurality of data integration tasks, a plurality of point-to-point data integration tasks defining a data integration transformation plan to include: generating one or more publication data integration tasks comprising publishing from each respective data source of the plurality of point-to-point data integration tasks to generate a single publication topic; and generating one or more subscription data integration tasks causing each respective target of the plurality of point-to-point data integration tasks to subscribe to the single publication topic; and generating a set of optimization instructions configured to cause the at least one computer to implement the data integration transformation plan; and executing the set of optimization instructions to generate the one or more publication data integration tasks and the one or more subscription data integration tasks.

RELATED APPLICATION DATA

This application is a continuation of U.S. patent application Ser. No.16/715,414 filed Dec. 16, 2019, the disclosure of which is herebyincorporated by reference in its entirety.

BACKGROUND

In the modern economy, data stores are often the most valuable asset afirm may possess. Many highly valuable data stores store extremely largeamounts of data, in many disparate physical data storage facilities,each containing numerous separate and distinct data stores, whichthemselves contain large amounts of data in various forms, such asfiles, relational databases, hierarchical databases, non-relationaldatabases, and the like.

A firm's data store may be interconnected by a network, or multiplenetworks, with a number of application servers and workstations runningapplications that interact with, operate on, and retrieve data from thefirm's data store by interacting with, operating on, and retrieving datafrom specific individual data sources, e.g., files, specific databases,or specific datasets, such as database tables, specific database tablecolumns, hierarchies, sub-hierarchies, non-relational data units, orother individual data storage units. An application may so create,manage, or rely upon various data integration tasks between manydifferent datasets stored within a data store's data storage facilities.And such applications may also create new data structures within one ormore data stores of the data store, that themselves are the source forother applications or data sources.

Data use trends indicated that the more data a firm can collect and makeefficient use of its data, and better it is ability to conductoperations, the better the firm is able to support its customers andclients. Thus, firms are expending large amounts of resources and timeto analyze and understand their data.

Data integration tasks, such as extraction, transformation, and loadingdata between data sources and between data sources and applications canbe described in a computer-parsable language, e.g., TransformationDefinition Language (TDL), that concisely describes and characterizesdata transformations within data stores. Firms may employ various toolsfor creating, maintaining, and governing a data store.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram illustrating aspects of a dataintegration tool in accordance with this disclosure.

FIG. 2 is a functional diagram illustrating aspects of a point-to-pointdata integration in accordance with this disclosure.

FIG. 3 is a flow chart illustrating an exemplary process in accordancewith this disclosure.

FIG. 4 is a flow chart illustrating an exemplary process in accordancewith this disclosure.

FIG. 5 illustrates aspects of a data integration collection inaccordance with this disclosure.

FIG. 6 is a functional diagram illustrating aspects of a point-to-pointdata integration in accordance with this disclosure.

FIG. 7 is a functional diagram illustrating aspects of a pub/subintegration in accordance with this disclosure.

FIG. 8 is a functional diagram illustrating aspects of a point-to-pointdata integration in accordance with this disclosure.

FIG. 9 is a functional diagram illustrating aspects of a pub/subintegration in accordance with this disclosure.

FIG. 10 is a functional diagram illustrating aspects of a point-to-pointdata integration in accordance with this disclosure.

FIG. 11 is a functional diagram illustrating aspects of a pub/subintegration in accordance with this disclosure.

FIG. 12 is a functional diagram illustrating aspects of a point-to-pointdata integration in accordance with this disclosure.

FIGS. 13A and 13B are functional diagrams illustrating aspects of apub/sub integration in accordance with this disclosure.

FIG. 14 illustrates aspects of a data integration transformation plan inaccordance with this disclosure.

FIG. 15 is a flow chart illustrating an exemplary process in accordancewith this disclosure.

FIG. 16 is a flow chart illustrating an exemplary process in accordancewith this disclosure.

FIG. 17 is a flow chart illustrating an exemplary process in accordancewith this disclosure.

FIG. 18 is an illustration of an exemplary empty integration map inaccordance with this disclosure.

FIG. 19 is an illustration of an exemplary populated integration mapaccording with this disclosure.

FIG. 20 is an illustration of a data integration optimization inaccordance with this disclosure.

FIG. 21 is a flow chart illustrating an exemplary process in accordancewith this disclosure.

FIG. 22 is an illustration of an exemplary populated integration mapaccording with this disclosure.

FIG. 23 is an illustration of a data integration optimization inaccordance with this disclosure.

FIG. 24 is functional diagram of a computer processing machine inaccordance with this disclosure.

DETAILED DESCRIPTION

Disclosed are one or more embodiments that incorporate features of thisinvention. The disclosed embodiment(s) merely exemplify the invention.The scope of the invention is not limited to the disclosed embodiments.Rather, the invention is defined by the claims hereto.

Firms may employ various tools for creating, maintaining, and governinga data store. One such tool is a data integration collection, which mayinclude among other things a description of a firm's data integrationtasks in, e.g., TDL, or another data integration language as describedbelow, and may also include one or more interfaces for accessing,creating, modifying, and deleting data a firm's data integration tasks.

Given the ad hoc nature in which many data integration tasks arise in afirm's data store, individual data units may be stored in duplicativelocations by the various applications that rely on such data. And the adhoc nature of creating data integration tasks within a data store leadsto “integration hairballs” that are not scalable, and thereforeinefficient in terms of resources consumed, and also difficult andcostly to maintain and govern. Such integration hairballs arise when adata source within a data store is used multiple times or for unrelatedpurposes, e.g., by multiple applications or processes.

An exemplary basic data integration task includes extracting data from asource, performing a transformation on the data, and then loading thetransformed data to a target. This process is succinctly described,e.g., in TDL, among other suitable languages. The various dataintegration tasks of a firm may be cataloged in a data integrationcollection, which may include for each data integration tasks, adescription a source, one or more transformations, and a target. A firmmay create, delete, and manage such data integration tasks using variousdata integration tools that interact or components of a data storemanagement platform. For example, Informatica's Intelligent CloudServices provides management tools and APIs for managing a data store invarious cloud configurations.

In some embodiments, a data integration tool executes in a cloudenvironment, which may exist in a hybrid environment or a fullyoff-premises environment. A data store provides an API, e.g., REST API,which is configured to provide access to client processes seeking toaccess information from a data store. A data integration tool may beconfigured create, update, delete data integration tasks, and may alsobe used for other tasks, such as configuring permissions associated witha data integration task. In interrelated embodiments, a data store mayprovide one or more APIs; e.g., in an embodiment a data store provides aJava Database Connectivity (JDBC) API. Metadata describing data and dataintegration tasks may be queried, e.g., using SQL queries.

Upon reading this disclosure one will readily appreciate that anembodiment of a data integration tool, e.g., data integration tool 112,includes instruction, e.g., instructions describing DI application 122,configured to cause a computing device to interface to and configuredata integration tasks within a data store, e.g., data store 102, whichmay be a cloud based data store. One will further appreciate thatinstructions in accordance with this disclosure may take many forms inmany languages, e.g., in an embodiment a data integration tool islargely executes software coded instructions written in Java and C++.One will also appreciate that any particular embodiment of instructionsin accordance with this disclosure may be written in a single languageor in a variety of languages, as is left to a designer based on designconsiderations specific to an application. One will further appreciatethat, upon reading this disclosure, generating instructions embodyingaspects of this disclosure may be accomplished using understood softwaredevelopment techniques.

FIG. 1 illustrates various aspects 100 of an exemplary firm's dataprocessing resources. A data store 102 comprises a plurality of dataservices, e.g., 104 a-104 e, each comprising one or more data sources,e.g., 134, which are relied upon by various data users, e.g.,Applications A-C 108 a-108 c, all of which is interconnected by anetwork 110 (which may be multiple networks, and may include theInternet). These aspects may be geographically collocated or may begeographically distributed, and each respective application, e.g., 108a-108 c, may be a part of or be integrated with one or more datasources, e.g., 134, of the data store 102. In an embodiment, a firmsdata processing resources are configured in a cloud type configuration.

In an embodiment, a firm's data store and a firm's applications areanalyzed according to known methods to identify data integration tasks,which may be cataloged in a data integration collection. For example, aFirm may employ a data integration tool 112, which may be one of afirm's applications. An exemplary data integration tool, in someembodiments, includes a data integration collection 114 stored on amemory accessible by one or more processors 118 coupled to a memory 120storing a data integration application 112; e.g., data integrationapplication 112 may be loaded in memory 120 by one or more processors118 from a persistent data store. DI Application 122 may be a set ofinstructions configured to cause the processor to carry out varioustasks in accordance with this disclosure. In some embodiments, dataintegration collection 114 is stored in data store 102 and retrieved byprocessor 118. A data integration tool has a variety of inputs andoutputs 124, such as a user interface for accepting input from one ormore users (man or machine) and one or more network interfaces as onewould expect of a modern computer system.

In an embodiment, DI application 122 may be configured to catalog dataintegration tasks, e.g., data integration tasks 200, of a firm in a dataintegration collection 114 and then perform various techniques inaccordance with this disclosure in order to optimize data integrationtasks in order to ensure that they are scalable and easily manageableand are optimized for a firm's resources.

FIG. 2 illustrates various exemplary un-optimized data integration tasks200 within a firm's data store, e.g., collection 102. The various dataintegration tasks 200 may be relied up by one or more data users, e.g.,data users 106. FIG. 2 illustrates a simple data integration hairball,in which a plurality of data stores 202, 204, 206, 208, 210, and 212,are either data sources or targets (or both) of a plurality ofpoint-to-point data integration tasks, e.g., data integration task 216.Data integration tasks illustrated in FIG. 2 with like arrows areintended to indicate data integration tasks having a commonality, whichmay be a same source, a same target, or a combination of same source andsame target. FIG. 2 also highlights that certain point-to-point dataintegration tasks may also include a data transformation, e.g., 220.

These tasks may, for example, be described in a standard language likeTDL. Another way to describe a data integration collection is throughrelational database structures that may be queried using in any standardquery language, such as SQL. In another embodiment, within a cloud baseddata integration collection, the collection may be accessed and queriedusing a API implementation, which provides a standard mechanism fordescribing a data store including data integration tasks. In general,upon reading this disclosure, one will appreciate that there are manyways to model a data integration collection and many ways to access andfetch information about data integration tasks from a data integrationcollection, and to apply changes to data integration tasks within a dataintegration collection.

A data integration engine may describe, and allow visualization of, dataflows from sources to targets, i.e. data integration tasks. A dataintegration engine, e.g., DI engine 122 a, may be configured to analyzethe flow of data between sources and targets and create or update a dataintegration collection to describe a current listing of allpoint-to-point data integration tasks, e.g., 216, within a data store,e.g., 102.

FIG. 3 illustrates a flow chart diagram illustrating an exemplaryprocess 300 for optimizing data integration tasks, e.g., dataintegration tasks 200 within a data store, e.g., data store 102. In afirst step 302, a data integration collection may be generated; in someembodiments, a data integration collection, e.g., 114, may already havebeen generated and may be maintained or modified using, e.g., a dataintegration tool such as tool 112. In a second step 304, a dataintegration map may be generated; as in step 302 a data integration mapmay alternatively have been previously generated and may be read ormodified. In step 306, an application, e.g., publication/subscriptionengine 122 b, may evaluate the collection of point-to-point dataintegration tasks to identify a sub-set of all the point-to-point dataintegration tasks for optimization. In an embodiment, the point-to-pointdata integration tasks, e.g., 216, may be evaluated for optimization byidentifying a commonality, such as a same source or a same target or aset of tasks associated with a same application (e.g., where differentapplications rely on data sets from a set of same or overlapping datasources and two or more applications request data from same sources atdifferent or random times). In other embodiments, optimization targetsmay be based on design choice or based on customized requirementsassociated with a specific data store, e.g., data flows that rely onslow connections, or data flows that rely on slow processors, or dataflows that occur on particular schedules.

In a second step 308, based on the sub-set of data integration tasks adata integration transformation plan is generated. A data integrationtransformation plan describes how a sub-set of point-to-point dataintegration tasks may be optimized by transformation to apublication/subscription, or pub/sub, model. Thus, for a data flow froma source to a target, that may include a transformation for data, a dataintegration transformation plan will describe at least one publisher, apublication topic, that may include a transformation of data, and atleast one subscriber, as will be described in more detail below inreference to exemplary embodiments illustrated in FIGS. 5-14.

A publication topic is a data service to which one or more publishersmay public data to be subscribed to by one or more subscribers. Apublication topic may be a data repository, e.g. a database, configuredto store all of the data that previously flowed from all thepoint-to-point sources to all the point-to-point targets. A publicationtopic may be defined to include a specification and structure of a dataservice and/or data store to serve as the physical location of thepublication topic, and the publication topic may further be defined toinclude a schedule upon which data is published from the one or moresources to the publication topic. In an embodiment, a generated dataintegration transformation plan includes the physical location of theintended publication topic and a schedule upon which the publication ispublished to the physical location. In some embodiments, the plan mayalso include a persistence period specifying a period of time duringwhich a particular instance of the publication is persisted after whichit is deleted. In some embodiments, a persistence period may beundefined or infinite while in other embodiments, it may be persistedfor a period on the order of seconds, minutes, hours, days, weeks,months, or years. In some embodiments, a resulting data integrationtransformation plan may optionally be provided to a user for evaluation,modification, editing, and/or approval. In some embodiments, apublication topic is one or more relational database tables. In otherembodiments, a publication topic is a flat file. In some embodiments, apublisher publishes one or more data structures as one or more blobs ofdata in a publication topic and subscribers subscribe to such blobs ofdata, and are configured to interpret the blobs of data published by apublisher. In some exemplary embodiments, a publisher publishes aplurality of relational database tables to a publication topic whichstores the publication as a plurality of relational database tables thatare subscribed to by subscribers.

In a step 310, a set of optimization instructions may be generated fortransforming the data integration relying on point-to-point dataintegration tasks to a pub/sub model data integration. Any suchgenerated instructions will be specific to a data store, to the dataintegration tasks identified for optimization, and to the dataintegration engine relied upon, provided that upon reading thisdisclosure and fully appreciating this disclosure, generation of suchinstructions will be a matter of design and implementation. In a step312, the optimization instructions may be executed by a processor totransform selected point-to-point data integration tasks to obtainoptimized data integration tasks in a pub/sub configuration.

FIG. 4 illustrates a flow chart diagram illustrating an exemplaryprocess 400 for optimizing data integration tasks, e.g., dataintegration tasks 200 within a data store, e.g., data store 102. In afirst step 402, one or more publication data integration tasks aregenerated, such that each of the one or more publication dataintegration tasks are configured to publish data from a source of apoint-to-point data integration tasks to a publication topic. In one ormore embodiments, a publication data integration tasks includesidentifying a physical location and structure for a publication topic, apersistence schedule, and one or more data transformations that arerequired to create a publication topic.

In a second step 404, one or more subscription data integration tasksare generated, such that each of the one or more subscription dataintegration tasks are configured to subscribe to a publication topic. Inone or more embodiments, these tasks may further include specificationof which data fields within a publication topic a particularsubscription data integration task subscribes to. A subscription dataintegration task may also include a schedule upon which a subscriptiontasks occurs. In some embodiments, rather than perform a datatransformation during publication of a publication topic (e.g., asfurther discussed in relation to FIG. 13B), a subscription dataintegration task may instead include a data transformation task (e.g.,as further discussed in relation to FIG. 13A). In this way, data storedin a publication topic may be untransformed and a target subscribing toparticular data within a publication topic may cause a transformationduring a subscription tasks.

FIG. 5 illustrates aspects of an exemplary data integration collection502 listing a set of illustrative point-to-point data integration tasks504 prior to optimization. For the purpose of illustrating aspects of adata integration transformation, data integration collection 502illustrates various data integration task grouped, for ease ofunderstanding, based on integration asks having commonalities. In afirst example, data integration tasks 504 a, 504 b, and 504 c each sharea common source. In a second example, data integration tasks 504 d, 504e, and 504 f each share a common target. In a third example, dataintegration tasks 504 g, 504 h, and 504 i each ultimately share a commonsource. In a fourth example, data integration tasks 504 j, 504 k, 504 l,and 504 m ultimately share a common target.

FIG. 6 illustrates a an exemplary set of point to point data integrationtasks 600 corresponding to catalog entries 504 a, 504 b, 504 c. Each ofthese integration tasks include a point-to-point data flow 602 a, 602 b,602 c from common data source A 202 to data targets B 204, C 206, and D208. According to an exemplary data transformation, the point-to-pointdata integration tasks 600 may be optimized to a pub/sub model 700 asillustrated in FIG. 7. As illustrated in FIG. 7, in a single publication704, data source A 202 publishes data to a single publication topic T₁702, and data targets 204, 206, 208 each subscribe to the singlepublication topic 702 through subscription data tasks 706 a, 706 b, 706c. This data publication/subscription model is further described by dataintegration transformation plan 1402 data integration tasks 1404 a, 1404b, 1404 c, 1404 d. A data integration transformation plan may begenerated and presented to a user, e.g., a human user in human readableform as in the following illustrative example:

For data integration tasks:

-   -   Data Source A->Data Target B    -   Data Source A->Data Target C    -   Data Source A->Data Target D

Generate data publication integration tasks:

-   -   Data Source A->Topic Publication T₁

Generate data subscription integration tasks:

-   -   Topic Publication T₁->Data Target B    -   Topic Publication T₁->Data Target C    -   Topic Publication T₁->Data Target D        Optionally, a data integration transformation plan also includes        a step of deleting, removing, or disabling the un-optimized data        integration tasks, e.g.:

Delete:

-   -   Data Source A->Data Target B    -   Data Source A->Data Target C    -   Data Source A->Data Target D        In some embodiments, such a plan is presented to a user for        approval or modification prior to carrying out the data        integration transformation plan to optimize a firm's data store        data integration. One will appreciate that the example human        readable form of a data integration plan above is intended for        exemplary purposes only, and various other suitable formats and        organizations of a human readable plan are possible.

For another example, FIG. 8 illustrates a set of point to point dataintegration tasks 800 corresponding to catalog entries 504 d, 504 e, 504f. Each of these integration tasks include a point-to-point data flow216 a, 216 b, 216 c from data sources A 202, B 204, C 206 to data targetD 208. In this example, target D 208 receives data from common sources A202, B 204, and C 206. According to an exemplary data integrationtransformation, the point-to-point data integration tasks 800 may beoptimized to a pub/sub model 900 as illustrated in FIG. 9. Asillustrated in FIG. 9, publication data flows 216 a, 216 b, and 216 cpublish data to a single publication topic T₂ 902, and data target 208subscribes to the single publication topic 902 through subscription datatask 908. This data publication/subscription model is further describedby data integration transformation plan 1402 data integration tasks 1404e, 1404 f, 1404 g, 1404 h. A data integration transformation plan may begenerated and presented to a user, e.g., a human user in human readableform as in the following illustrative example:

For data integration tasks:

-   -   Data Source A->Data Target D    -   Data Source B->Data Target D    -   Data Source C->Data Target D

Generate data publication integration tasks:

-   -   Data Source A->Topic Publication T₂    -   Data Source B->Topic Publication T₂    -   Data Source C->Topic Publication T₂

Generate data subscription integration tasks:

-   -   Topic Publication T₂->Data Target D        Optionally, a data integration transformation plan also includes        a step of deleting, removing, or disabling the un-optimized data        integration tasks, e.g.:

Delete:

-   -   Data Source A->Data Target D    -   Data Source B->Data Target D    -   Data Source C->Data Target D

For another example, FIG. 10 illustrates a set of point to point dataintegration tasks 1000 corresponding to catalog entries 504 h, 504 i,504 j. Each of these integration tasks include a point-to-point dataflow 1002 a, 1002 b, 1002 c from data sources A 202, and intermediaterepository Y 212, to data targets B 204, and C 206. In this example, adata connection between A and B may be relatively slow, or, either datasource A or data target B may be subject to heavy usage during a certainperiod, such that it is preferable to first flow data from A 202 to Y212 via point-to-point data flow 1002 a at a particular time to stagedata at intermediate repository Y 212 until data is flowed to datatarget B 204. Thus, data flows 1002 a-1002 c are ultimately related bycommon sources, A 202. According to an exemplary data transformation,the point-to-point data integration tasks 1000 may be optimized to apub/sub model 1100 as illustrated in FIG. 11. As illustrated in FIG. 11,publication data flow 1104a publishes data to a single publication topicT₃ 1102, and data targets 204, 206 each subscribe to the singlepublication topic 1102 through subscription data tasks 1106 a, 1106 b.This data publication/subscription model is further described by dataintegration transformation plan 1402 data integration tasks 1404 j, 1404k, 1404 l. A data integration transformation plan may be generated andpresented to a user, e.g., a human user in human readable form as in thefollowing illustrative example:

For data integration tasks:

-   -   Data Source A->Data Target Y    -   Data Source Y->Data Target B    -   Data Source A->Data Target C

Generate data publication integration tasks:

-   -   Data Source A->Topic Publication T₃

Generate data subscription integration tasks:

-   -   Topic Publication T₃->Data Target B    -   Topic Publication T₃->Data Target C        Optionally, a data integration transformation plan also includes        a step of deleting, removing, or disabling the un-optimized data        integration tasks, e.g.:

Delete:

-   -   Data Source A->Data Target Y    -   Data Source Y->Data Target B    -   Data Source A->Data Target C

For another example, FIG. 12 illustrates a set of point to point dataintegration tasks 1200. In an embodiment, two groups of dataintegrations tasks respectively sharing a commonality, e.g. samerespective applications, may also share a commonality, e.g., commonsources. As illustrated in FIG. 12, point-to-point data integrationtasks 216 a, 216 b, 216 c share a common set of sources withpoint-to-point data integration tasks 216 d, 216 e, 216 f. Thesepoint-to-point data integration tasks correspond to catalog entries 504d, 504 e, 504 f, 504 j, 504 k, 504 l. Each of these integration tasksinclude a point-to-point data flow 216 a, 216 b, 216 c, 216 d, 216 e,216 f from data sources A 202, B 204, C 206 to data targets D 208 and E210. In this example, targets D 208 and E 210 receive data from commonsources A 202, B 204, and C. Source C 206 provides data to target D 208,and provide similar data to target 210 that is first transformed bytransform operation 260. In an embodiment, targets D 208 and E 210 areintended to collect a list of all customer names in sources A, B, and C.For example, First Name and Last Name flow from A 202, B 202, and C 206to D 208 by point-to-point data flow 216 c; and the same First_Name andLast_Name flow from A 202, B 202, and C 206 to E 210 via three separatedata integration tasks 216 d, 216 e, 216 f subject to a transformation,e.g. a concatenation of fields from A 202, B 202, and C 206 includingFirst_Name and Last_Name such that a field Full_Name is received into E210. Thus, data flows 216 a-216 f are related by common sources, A 202,B 204, and C 206, but store the customer names differently. According toan exemplary data transformation, the point-to-point data integrationtasks 1200 may be optimized to a pub/sub model 1300 as illustrated inFIG. 13A. As illustrated in FIG. 13A, publication data flows 904 a, 904b, and 904 c publish data to a single publication topic T₂ 902, and datatargets 208, 210 each subscribe to the single publication topic 902through subscription data tasks 1306, 908. This datapublication/subscription model is further described by data integrationtransformation plan 1402 data integration tasks 1404 e, 1404 f, 1404 g,1404 h, 1404 m, 1404 n. As one will appreciate, when optimizingexemplary hairball integration 200, exemplary pub/sub model 1300includes exemplary pub/sub model 900 and additional optimization suchthat publication topic T₂ contains data subscribed to by data targets D208 and E 210. An exemplary data integration transformation plan forthis example embodiment discussed above involving customer names may begenerated and presented to a user, e.g., a human user in human readableform as in the following illustrative example:

For data integration tasks:

-   -   Data Source A(FN, LN)->Data Target D(FN,LN)    -   Data Source B(FN, LN)->Data Target D(FN, LN)    -   Data Source C(FN, LN)->Data Target D(FN, LN)    -   Data Source A(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source B(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source C(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)

Generate data publication integration tasks:

-   -   Data Source A(FN, LN)->Topic Publication T₂(FN, LN)    -   Data Source B(FN, LN)->Topic Publication T₂(FN, LN)    -   Data Source C(FN, LN)->Topic Publication T₂(FN, LN)

Generate data subscription integration tasks:

Topic Publication T₂(FN, LN)->Data Target D(FN, LN)

Topic Publication T₂(FN, LN)->Transform(FN, LN=>FULL)->Data TargetE(FULL)

Optionally, a data integration transformation plan also includes a stepof deleting, removing, or disabling the un-optimized data integrationtasks, e.g.:

Delete:

-   -   Data Source A(FN, LN)->Data Target D(FN,LN)    -   Data Source B(FN, LN)->Data Target D(FN, LN)    -   Data Source C(FN, LN)->Data Target D(FN, LN)    -   Data Source A(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source B(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source C(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)

According to an alternative data transformation plan 1422, dataintegrations 1200 are transformed to a data publication datasubscription model 1300b, which includes performing transformation 1320during publications rather than during a subscription task, e.g., 1306.If, for example, data target E 210 is a slow resource such that it isdesirable to shift resources for performing data processing off of adata service providing target E 210, it may be desirable to publish bothtransformed and untransformed data from A 202, B 204, C 206 topublication topic T₂ 902. This may result in an alternative datatransformation plan 1422 including data publication tasks 1424 a-1424 fand data subscription tasks 1424 g and 1424 h. An exemplary dataintegration transformation plan for this example embodiment may begenerated and presented to a user, e.g., a human user in human readableform as in the following illustrative example:

For data integration tasks:

-   -   Data Source A(FN, LN)->Data Target D(FN ,LN)    -   Data Source B(FN, LN)->Data Target D(FN, LN)    -   Data Source C(FN, LN)->Data Target D(FN, LN)    -   Data Source A(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source B(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source C(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)

Generate data publication integration tasks:

-   -   Data Source A(FN, LN)->Topic Publication T2 (FN, LN)    -   Data Source B(FN, LN)->Topic Publication T2(FN, LN)    -   Data Source C(FN, LN)->Topic Publication T2(FN ,LN)    -   Data Source A(FN, LN)->Transform(FN, LN=>FULL)->Topic        Publication T2(FULL)    -   Data Source B(FN, LN)->Transform(FN, LN=>FULL)->Topic        Publication T2(FULL)    -   Data Source C(FN, LN)->Transform(FN, LN=>FULL)->Topic        Publication T2(FULL)

Generate data subscription integration tasks:

-   -   Topic Publication T₂(FN, LN)->Data Target D(FN, LN)    -   Topic Publication T₂(FULL)->Data Target E(FULL)        Optionally, a data integration transformation plan also includes        a step of deleting, removing, or disabling the un-optimized data        integration tasks, e.g.:

Delete:

-   -   Data Source A(FN, LN)->Data Target D(FN,LN)    -   Data Source B(FN, LN)->Data Target D(FN, LN)    -   Data Source C(FN, LN)->Data Target D(FN, LN)    -   Data Source A(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source B(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)    -   Data Source C(FN, LN)->Transform(FN, LN=>FULL)->Data Target        E(FULL)

Additionally, different data sources may store similar data in differentformats. A variety of algorithms may ultimately be used to identifysource groups and target groups having a commonality. For example,cluster analysis techniques such as K-means clustering may be employed,whereas Jaro-Winkler distances may be employed for identifying commonstring based data. In an embodiment, various hierarchical database maybe analyzed for commonalities by first projecting hierarchicalsemi-structured sources into relational representations, which then canbe treated like other relational tables.

FIG. 14 illustrates a data integration transformation plan 1402 foroptimizing the data integration described in data integration collection502. In an embodiment, a data integration transformation plan may alsoinclude details (not particular depicted) regarding data integrationtasks to be removed, disabled, or deleted. And in some embodiments, adata integration collection may be updated to reflect an optimized dataintegration as a result of a data integration transformation plan suchas plan 1402.

FIG. 15 illustrates a process 1500 that is interrelated with embodimentsillustrated in FIGS. 1-14. In a first step 1502, data is published to apublication topic, e.g. 702, 902, 1102 in exemplary embodiments above.In step 1502, data published to a publication topic may be publishedfrom one or more sources. In a second step 1504, a publication topic issubscribed to. In step 1504, one or more data targets may subscribe to apublication topic thereby obtaining or retrieving or receiving data froma publication topic. In a third step 1506, it is determined that allsubscribers to a publication topic have subscribed to, e.g., obtained orreceived, respective data from a publication topic, and at step 1508 allsubscriptions having been fulfilled a publication topic is deleted. Whenan event causing a publication reoccurs at step 1510, steps 1502, 1504,1506, and 1508 are repeated. In an interrelated embodiment, datapublication in step 15202 may occur according to a schedule, such thatdata is published periodically according to a schedule. In aninterrelated embodiment, an event may be a trigger, which may be anamount of new data in a data source, or which may be an external event,or a scheduler. In an interrelated embodiment, data subscribers to apublication topic may occur according to a schedule, such that data isobtained, or received, or retrieved according to a schedule at ascheduled period of time. In an embodiment, where a publication occursaccording to a schedule, subscriptions may be scheduled to occur after apublication is scheduled to occur. In an embodiment, step 1508 is anoptional step, as a system architect may decide to retain data within apublication topic for a predetermined amount of time (or indefinitely)after subscribers have obtained published data.

FIG. 16 illustrates an process 1600 that is interrelated with exemplaryembodiments illustrated in FIGS. 1-15. In a first step 1602, it isdetermined that a plurality of sub-sets of point-to-point integrationtasks are selected, e.g., to be optimized in accordance with thisdisclosure. In an embodiment, a determination in step 1602 is based onan analysis of a data integration collection in accordance with thisdisclosure. At step 1604, a plurality of single publication topics aregenerated, each respectively corresponding to one of a plurality ofsub-sets of point-to-point integration tasks. In some embodiments,respective sub-sets are selected based on a determination that each dataintegration task member of a respective subset shares one or morerespective commonalities. A plan is generated at step 1606 to transformeach of a plurality of sub-sets of point-to-point data integration tasksto sets of subscription data integration tasks, each includingsubscribing to a single publication topic. In an optional step 1608, adata integration transformation plan is presented to a user formodification and/or approval. In step 1610, each of the plurality ofsubsets of point-to-point data integration tasks is transformed into aset of subscription data integration tasks such that each respectivedata target subscribes to at least one single publication topic.

FIG. 17 is a flow chart 1700 illustration of a process in accordancewith this disclosure. In a first operation 1702, a list L of dataintegration tasks is obtained (or received, or retrieved). Inembodiments, a list L of data integration tasks may be retrieved from adata integration collection, e.g., 114. List L may, e.g., be a linkedlist containing a list of data integration tasks. In some embodiments, adata integration collection is first created by querying a data store,e.g., 102, via an API such as REST API or JDBC. In other embodiments, adata integration collection already exists. Having obtained list L ofdata integration tasks, in some embodiments, a same source strategy isimplemented. In operation 1704, a same source integration map M isgenerated. For example, FIG. 18 illustrates an exemplary dataintegration map M 1800, which may be employed in a same sourceoptimization strategy. Exemplary map M 1800 contains a list of keys 1810associated with a list of integration tasks. In embodiments map 1800 isinitialized as an empty key-value map. In operation 1706, a first dataintegration task t is evaluated, and the source of data integration taskt, source(t), is checked against keys, e.g., 1810 of map M 1800. If nokey in map M corresponds to source(t), in operation 1708 source(t) isadded as a key to map M. In some embodiments, a key is formed fromsource(t), e.g., by applying a suitable hash algorithm to source(t) togenerate a key value to add to the list of keys, e.g., 1810, in a map M1800. One suitable key may be generated as follows:

-   -   Hash(Source)=concat(source.connection.id, source.object.name)        One will appreciate upon reading this disclosure that many        suitable hash functions may be employed as a matter of design        considerations.

In operation 1710, data integration task t is added to map M as a valueassociated with a key corresponding to source(t). In operation 1711, ifall tasks from list L have been added to map M, the process proceeds tooperation 1716; if not all tasks are in map M, the process returns tooperation 1706, and another task is evaluated to determine if source(t)is in map M. If yes, in operation 1712, task t is added to map M as avalue associated with a key corresponding to source(t). When all taskshave been determined to have been added to map M in operation 1714, theprocess proceeds to operation 1716, and generating a data integrationplan is performed.

In operation 1719, for a key in map M, a count of associated tasks isdetermined, and measured against a threshold x. In some embodiments,x=1, a trivial case. In other embodiments, x may be greater than 1 asdetermined by a system designer. If a number of tasks associated with akey is not greater than x, in operation 1720, the key is skipped. If allkeys from map M have been considered, in operation 1722, the processflows to step 1732, if not the process returns to operation 1718, andanother key from map M is evaluated. If a number of tasks associatedwith a key in map M is greater than threshold x, a corresponding sourceis obtained from key in operation 1724. In operation 1726, a publicationtopic T is recommended based on source structure, and in operation 1728each target of each task value associated with the current key from mapM is identified and a subscription task subscribing to publication topicT is recommend. If not all keys in map M have been considered, theprocess returns to operation 1718. If all keys have been considered, inoperation 1732 a data integration transformation plan is recommendedbased on the publication topic T and subscription tasks recommended inoperations 1726, 1728. If approved, in operation 1734, the datatransformation plan is implemented.

FIG. 19 illustrates an exemplary data integration map 1900 comprisingkeys 1910 including HASH(A) and HASH(E). Key HASH(A) is associated withthree tasks TASK(A→B), TASK(A→C), TASK(A→D), and HASH(E) is associatedwith one task TASK(E→F). In this example, during operation 1718, if x=1,HASH(E) is a key that would be skipped, but HASH(A) would result in apublication/subscription strategy resulting, in step 1734, withtransformation 2000 in FIG. 20, where point to point data integrationtasks 2002 are transformed to a publication subscription model dataintegration 2004. In embodiments, values, e.g., 1820, 1920, may be addedas JAVA objects describing data integration tasks.

In one exemplary embodiment, instructions of a data integration tool,e.g., tool 112, may include the following exemplary instructions foradding tasks to a integration map, e.g., 1900, e.g., during operations1708, 1710:

Map<String, List<Task>> srcObjects = new HashMap<>( ); for (Task task :tasks) {  srcObjects.computeIfAbsent(task.hash( ), k -> new ArrayList<>()). add(task); }

It is possible that data integration tasks may be duplicative, e.g.,where a task may be copied for backup or versioning purposes, in whichcases, it may be desirable to optionally filter duplicative tasksfollowing operation 1702.

In one exemplary embodiment, during operation 1734, a publication topicmay be generated in accordance with the following exemplaryinstructions, which may be instructions of a data integration tool,e.g., tool 112:

URL : /dih-console/uiapi/v1/topics/create Request Payload:{“categories”:[ ], ”dataWriteAllowed”:true,”defaultStorageLocation”:true, ”deprecated”:false,”description”:”“,”discardDelayedEvents”:false,”eventAggregatedStatus”:{“hasDelayedEvents” :false,”hasNonFinalEvents”:false, ”hasNonFinalNonDelayedEvents”:false},”externalId”:”“,” lastModifiedDate”:”2019-12-02T16:17:48.127Z”,”partitioned”:false, ”publicationRepositoryType ”:“RDBMS”,”publisherCount”:0, ”retentionPeriod”:7, ”status”:”VALID”,”storageLocation”: ”USERS”,”subscriberCount”:0, ”topicId”:-1,”topicName”:”test123”,”topicReadOnly”: false, ”topicType”:“Delta”,”unstructured”:false,”writePermitted”:false,”canonicalStructure”:{“schemas”:[{“name” : ”“,”tables”:[{“tableName”:”table1 ”,”columns”:[{“columnName”: ”field1 ”,”typeName”:”STRING”,”primaryKey”:false, ”nullable”:true,”scale”:- 1,”length”:255,”filterAccelerator”:false,“isEncrypted”:false,”systemDefinedAccelerator”:false},{“columnName”:”field2”,”typeName” :”STRING”,”primaryKey”:false,”nullable”:true,”scale”:-1,”length”:255,”filterAccelerator” :false,”isEncrypted”:false,”systemDefinedAccelerator”:false},{“columnName”:”DIH_PUBLICATION_INSTANCE_DATE”,”typeName”:”PUBLICATION_DATE_FIELD_TYPE”,”primary Key”:false, ”nullable”:false, ”scale”:-1,”length”:-1,”filterAccelerator”:true, ”isEncrypted”: false,”systemDefinedAccelerator”:true},{“columnName”:”DIH_PUBLICATION_INSTANCE_ID”,”typeName”:”PUBLICATION_INSTANCE_ID_FIELD_TYPE”,”primaryKey”:false,”nullable” :false,”scale”:0,”length”:19,”filterAccelerator”:true,”isEncrypted”:false, ”systemDefined Accelerator”:false}]}]}],”customMetadata”:{}}}

In one exemplary embodiment, during operation 1734, one or more topicsubscriptions may be generated in accordance with the followingexemplary instructions, which may be instructions of a data integrationtool, e.g., tool 112:

URI: /dih-console/uiapi/v1/subscriptions Request Payload:{“allowDiscardEvents”:true, ”applicationId”:3956,”applicationName”:”employee”,”customWorkflowName”:”“,”dataWriteAllowed”:true,”deliveryPreferencesType”:”ALL_AVAILABLE_DATA”,”description”:”“,”enableStatus”:”ENABLE”,”endpointType”:”UNKNOWN”,”eventAggregatedStatus ”:{“hasDelayedEvents”:false, ”hasNonFinalEvents”:false,”hasNonFinalNonDelayed Events”:false}, ”externalId ”:”“,”icsTask”:{“id”: ”010UQX0I000000000003 ”,”orgId”:”010UQX ”, ”name ”: ”empSub ”,”description”: “”,”updateTime”: ”“,”createdBy”:”cihilabs”,”updatedBy ”:”cihilabs”,”taskType”:”DSS”,”taskTypeDescription”:”DataSynchronization”}, ”icsTaskName”:”empSub”,“insertStrategy”:”APPEND”,”isPushDown”:false, ”isSorted”:false,”isOnlineSubscription”:false, ”mappingType”:”CUSTOM_WORKFLOW”,”numberOfPartitions”:1, ”pubArrivalTimeFrameInHours”:1, ”schedule”:{“cronExpression”:”0 45 17 * * ? ”, ”cronExpressionDetails”:{“daily_hours ”: “0 ”, ”daily minutes ”: ”0 ”,”hourly_interval”:”1”,”minutely_interval”:”1”,”monthly_at_day”:”1”,”monthly_expression_placing”:”FIRST”,”monthly_expression_weekday”:”DAY”,”monthly_hours”:”0”,”monthly_minutes”:”0 ”, ”monthly_recurrence”:”EXPRESSION”,”recurrence”:”MINUTELY”,”secondly_ minutes”:”5”,”secondlyseconds”:”0”,”used ”:true,”weekly_friday”:false,”weekly_hours”:”0”,”weekly_minutes”:”0”,”weekly_monday”:true, ”weekly_saturday ”:false,”weekly_sunday ” :false, ”weekly_thursday”:false, ”weekly_tuesday”:false, ”weekly_wednesday ”:false}, ”description ”: null, ”scheduleId”:null, ”scheduleName”:null, ”status”: ”ENABLED”}, ”status ”: ”INVALID”, ”subscriptionName”:”sub123”,”topics”:[{“topicId”:3337,”topicName”:”employee”,”description”:”“,”dataWriteAllowed”:false, ”topicReadOnly”:true,”writePermitted”:false, ”topicType ”:”Delta”,”publicationRepositoryType”:”RDBMS”,”status”:”VALID”,”externalId”:”DIH_top_employee”,”canonicalStructure ”: [{“endpoint Type”:”RDBMS”,”schemas”:[{”name”:”employee ”,”empty”:false,”tables”:[{“tableName”:”emp”,”columns”:[{“isEncrypted”:false,”dihInternal Field ”:false, ”binaryType ”:false, ”name”: ”id”,”columnName”:”id”,”typeName”:”INT64”, ”primaryKey”:false, ”nullable”:true, ”scale ”:-1, ”length ”:-1,”filterAccelerator ”:true,systemDefinedAccelerator ”:false}, {“isEncrypted”:false,”dihInternalField”:false, ”binaryType”:false,”name”:”name”,”columnName”:”name”,”typeName”:”STRING”,”primaryKey”:false, ”nullable ”:true, ”scale”:-1, ”length”:255,“filterAccelerator”: false, ”systemDefinedAccelerator”:false},{“isEncrypted”:false, ”dihInternalField”:false, ”binaryType”:false, ”name ”: ”age ”, ”columnName ”:”age ”,”typeName ”:”DECIMAL”,”primaryKey ”: false, ”nullable”:true, ”scale ”:0, ”length ”:3 ,”filterAccelerator”:false , ”systemDefinedAccelerator”:false},{”isEncrypted”:false, ”dihInternalField”:false,”binaryType”:false, ”name ”:”city”, ”columnName ”:”city ”,”typeName”:”STRING”,”primaryKey ”:false, ”nullable ”:true, ”scale”:- 1,”length ”: 50, ' filterAccelerator”:true,”systemDefinedAccelerator”:false}, {“isEncrypted”:false,”dihlnternalField”:false, ”binary Type”:false, ”name”: ”salary”,”columnName ”:”salary”,”type Name ”:”DECIMAL ”,”primaryKey”:false,”nullable”:true,”scale”:2, ”length”:15,”filterAccelerator” :false,”systemDefinedAccelerator”:false}, {“isEncrypted”:false ,”dihInternalField”:true, ”binaryType”:false, ”name”:”DIH_PUBLICATION_INSTANCE_DATE ”, ”columnName”: ”DIH_PUBLICATION_INSTANCE_DATE”, ”typeName”:”PUBLICATION_DATE_FIELD_TYPE”,”primaryKey”:false, ”nullable”:false, ”scale”:-1,”length”:-1,”filterAccelerator” :true, ”systemDefinedAccelerator”:true},{“isEncrypted”:false, ”dihInternalField” :true,”binaryType”:false, ”name”: ”DIH_PUBLICATION_INSTANCE_ID”, ”columnName”:” DIH_PUBLICATION_INSTANCE_ID”, ”typeName”:”PUBLICATION_INSTANCE_ID_FIELD_ TYPE”, ”primaryKey”:false,”nullable”:false, ”scale”:0, ”length”:19,”filterAccelerator”:true,”systemDefinedAccelerator”:false}], ”name”: ”emp”, ”customMetadata”:{“CANONIC_TABLE_ STAGING_NAME”: ”emp_employee”,”CANONIC_TABLE_UTILITY_NAME”: ”emp_employee_ DIHUTILITY”}}]}],”singleSchema”:{“name”: ”employee”, ”empty”:false, ”tables”:[{ “tableName”: ”emp”, ”columns”:[{“isEncrypted”:false, ”dihInternalField”:false,”binaryType”:false, ”name”: ”id”, ”columnName”: ”id”, ”typeNam ”:”INT64”, ”primaryKey”:false , ”nullable”:true, ”scale”:- 1, ”length”:-1, ”filterAccelerator”:true, ”systemDefined Accelerator ”:false},{“isEncrypted”: false, ”dihInternalField”:false, ”binary Type”:false,”name”: ”name”, ”columnName”: ”name”, ”typeName”: ”STRING”,”primaryKey”:false, ”nullable”:true, ”scale”:- 1, ”length”:255 ,”filterAccelerator”:false, ”systemDefinedAccelerator”:false},{“isEncrypted” :false, ”dihInternalField”:false, ”binaryType”:false,”name”: ”age”, ”columnName”: ”age”, ”type Name”: ”DECIMAL”,”primaryKey”:false , ”nullable”:true , ”scale”: 0, ”length”:3 ,”filterAccelerator” :false, ”systemDefinedAccelerator”:false},{“isEncrypted”:false , ”dihInternalField”:false, ”binaryType ”:false,”name ”: ”city”, ”columnName”: ”city”,”typeName”: ”STRING”, ”primaryKey”:false , ”nullable ”:true, ”scale”:-1, ”length”:50,”filterAccelerator”:true, ”systemDefinedAccelerator”:false},{“isEncrypted”:false, ”dihInternalField”:false, ”binaryType”:false ,”name”: ”salary”, ”columnName”: ”salary”, ”typeName”: ”DECIMAL”,”PrimaryKey”:false , ”nullable”:true, ”scale”:2 , ”length”: 15,”filterAccelerator”:false, ”systemDefinedAccelerator”:false},{“isEncrypted”:false , ”dihInternalField”:true ,”binaryType”:false , ”name” :”DIH_PUBLICATION_INSTANCE_DATE”,”columnName”: ”DIH_PUBLICATION_INSTANCE_ DATE”, ”typeName”:”PUBLICATION_DATE_FIELD_TYPE”, ”primaryKey”:false, ”nullable :false,”scale ”:-1 , ”length”:-1 , ”filterAccelerator”:true,”systemDefinedAccelerator” :true),{“isEncrypted”:false,”dihInternalField”:true, ”binaryType”:false, ”name”: ”DIH_PUBLICATION_INSTANCE_ID”, ”columnName”: ”DIH_PUBLICATION_INSTANCE_ID ”,”typeName”: ”PUBLICATION_INSTANCE_ID_FIELD_TYPE”, ”primaryKey”:false,”nullable ”false, ”scale”: 0, ”length”: 19, ”filterAccelerator”:true,”systemDefinedAccelerator”:false}], ”name ”: ”emp”,”customMetadata”:{“CANONIC_TABLE STAGING_NAME”: ”emp_employee”,”CANONIC_ TABLE_UTILITY_NAME”: ”emp_employee_DIHUTILITY”}}]},”customMetadata”:{ }}, ”unstructured”:false , ”retentionPeriod”:7,”storageLocation”: ”PRIMARY”, ”categories”:[ ], ”eventAggregatedStatus”:{“hasNonFinalNonDelayedEvents”:false,”hasDelayedEvents”:false), ”discardDelayedEvents”:false,”lastModifiedDate”:1571630421430, ”lastStructuralChangeDate”:1571149485807, ”defaultStorageLocation”:true, ”deprecated”:false,”subscriberCount”:3, ”publisher Count”:1, ”partitioned”:false}],”triggerOption”: ”WHEN_PUBLISHED”, ”unboundSubscription” :false,”unstructured”:false, ”writePermitted”:true, ”batchSize”: ”1000”,”apiNotificationUrl” :”“}

FIG. 21 is a flow chart 2100 illustration of a process employing a sametarget strategy in accordance with this disclosure. In a first operation2102, a list L of data integration tasks is obtained (or received, orretrieved). In embodiments, a list L of data integration tasks may beretrieved from a data integration collection, e.g., 114. List L may,e.g., be a linked list containing a list of data integration tasks. Insome embodiments, a data integration collection is first created byquerying a data store, e.g., 102, via an API such as REST API or JDBC.In other embodiments, a data integration collection already exists.Having obtained list L of data integration tasks, in some embodiments, asame source strategy is implemented. In operation 2104, a same targetintegration map M is generated. For example, FIG. 22 illustrates anexemplary data integration map M 2200, which may be employed in a sametarget optimization strategy. Exemplary map M 2200 contains a list ofkeys 2210 associated with a list of sets of integration tasks. Inembodiments map 2200 is initialized as an empty key-value map. Inoperation 2106, a first data integration task t is evaluated, and thetarget of data integration task t, target (t), is checked against keys,e.g., 2210 of map M 2200. If no key in map M corresponds to target (t),in operation 2208 target (t) is added as a key 2210 to map M. In someembodiments, a key is formed from target (t), e.g., by applying asuitable hash algorithm to target (t) to generate a key value to add tothe list of keys, e.g., 2210, in a map M 2200. One suitable key may begenerated as follows:

-   -   Hash(Target)=concat(Target.connection.id, Target.object.name)        One will appreciate upon reading this disclosure that many        suitable hash functions may be employed as a matter of design        considerations.

In operation 2110, data integration task t is added to map M as a valueassociated with a key corresponding to target (t). In operation 2111, ifall tasks from list L have been added to map M, the process proceeds tooperation 2116; if not all tasks are in map M, the process returns tooperation 2106, and another task is evaluated to determine if target (t)is in map M. If yes, in operation 2112, task t is added to map M as avalue associated with a key corresponding to target (t). When all taskshave been determined to have been added to map M in operation 2114, theprocess proceeds to operation 2116, and generating a data integrationplan is performed.

In operation 2119, for a key in map M, a count of associated tasks isdetermined, and measured against a threshold x. In some embodiments,x=1, a trivial case. In other embodiments, x may be greater than 1 asdetermined by a system designer. If a number of tasks associated with akey is not greater than x, in operation 2120, the key is skipped. If allkeys from map M have been considered, in operation 2122, the processflows to step 2132, if not the process returns to operation 2118, andanother key from map M is evaluated. If a number of tasks associatedwith a key in map M is greater than threshold x, a corresponding sourceis obtained from key in operation 2124. In operation 2126, a publicationtopic T is recommended based on target structure and a subscription taskrecommending target (t) subscribe to topic T, and in operation 2128 eachsource of each task value associated with the current key from map M isidentified and a publication task publishing to publication topic T isrecommend. If not all keys in map M have been considered, the processreturns to operation 2118. If all keys have been considered, inoperation 2132 a data integration transformation plan is recommendedbased on the publication topics T and subscription tasks recommended inoperations 2126, 2128. If approved, in operation 2134, the datatransformation plan is implemented.

FIG. 22 illustrates an exemplary data integration map 2200 comprisingkeys 2210 including HASH(D) and HASH(F). Key HASH(D) is associated withthree tasks TASK(A→D), TASK(B→D), TASK(C→D), and HASH(F) is associatedwith one task TASK(G→F). In this example, during operation 2118, if x=1,HASH(F) is a key that would be skipped, but HASH(A) would result in apublication/subscription strategy resulting, in step 2134, withtransformation 2300 shown in FIG. 23, where point to point dataintegration tasks 2302 are transformed to a publication subscriptionmodel data integration 2304. In embodiments, values, e.g., 2220 may beadded as JAVA objects describing data integration tasks.

In one exemplary embodiment, during operation 2134, a publication topicbased on a same target strategy may be generated in accordance with thefollowing exemplary instructions, which may be instructions of a dataintegration tool, e.g., tool 112:

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[{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:″id″,″type″:″string″,″uniqueName″:″id″,″label″:″id″,″parentObject″:″employee/emp″,″pcType″:″NSTRING″,″precision″:20,″scale″:0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.lang.String″,″showLabel″:true,″naturalOrder″:0,″linkedFields″:[″EMPNO″],″relatedInfos″:[ ], ″references″:[]},″expression″:″EMPNO″),{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:″name″,″type″:″string″,″uniqueName″:″name″,″label″:″name″,″parentObject″:″employee/emp″,″pcType″:″NSTRING″,″precision″:255,″scale″:0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″.false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.lang.String″,″showLabel″:true, ″naturalOrder″:1,″linkedFields″:[″ENAME″],″relatedInfos″:[ ],″references″:[ ]},″expression″:″ENAME″},{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:″age″,″type″:″decimal″,″uniqueName″:″age″,″label″:″age″,″parentObject″:″employee/emp″,″pcType″:″DECIMAL″,″precision″:3,″scale″:0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.math.BigDecimal″,″showLabel″:true,″naturalOrder″:2, ″linkedFields″:[ ] ,″relatedInfos″:[],″references″:[ ]}},{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:″city″,″type″:″string″,″uniqueName″:″city″,″label″:″city″,″parentObject″:″employee/emp″,″pcType″:″NSTRING″,″precision″:50,″scale″:0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.lang.String″,″showLabel″:true,″naturalOrder″:3,″linkedFields″:[],″relatedInfos″:[ ],″references″ :[]}},{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:″salary″,″type″:″decimal″,″uniqueName″:″salary″,″label″:″salary″,″parentObject″:″employee/emp″,″pcType″:″DECIMAL″,″precision″:15,″scale″:2,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.math.BigDecimal″,″showLabel″:true,″naturalOrder″:4,″linkedFields″:[″SAL″],″relatedInfos″: [],″references″:[ ]},″expression″:″SAL″}],″sourceFields″:[{″@type″:″field″,″name″:″EMPNO″,″type″:″decimal″,″uniqueName″:″EMPNO″,″label″:″EMPNO″,″parentObject″:″EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:4,″scale″:0,″columnIndex″:0,″isKey″:true,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:false,″isUnique″:true,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabe″:false,″naturalOrder″:0,″linkedFields″:[″id″],″relatedInfos″:[ ],″references″:[ ]},{″@type″:″field″,″name″:″ENAME″,″type″:″varchar″,″uniqueName″:″ENAME″,″label″:″ENAME″,″parentObject″:″EMP″,″pcType″:″STRING″,'Precision″:10,″scale″:0,″columnIndex″:1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:1,″linkedFields″:[″name″] ,″relatedInfos″: [],″references″:[ ]},{″@type″:″field″,″name″:″JOB″,″type″:″varchar″,″uniqueName″:JOB″,″label″:″JOB″,″parentObject″:″EMP″,″pcType″:″STRING″,″precision″:9,″scale″:0,″columnIndex″:2,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:2,″linkedFields″:[], ″relatedInfos″:[ ],″references″:[]},{″@type″:″field″,″name″:″MGR″,″type″:″decimal″,″uniqueName″:″MGR″,″label″:″MGR″,″parentObject″:″EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:4,″scale″:0,″columnlndex″.3, ″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:3,″linkedFields″:[],″relatedInfos″:[ ],″references″:[ ]},{″@type″:″field″,″name″:″HIREDATE″,″type″:″timestamp″,″uniqueName″:″HIREDATE″,″label″:″HIREDATE″,″parentObject″:″EMP″,″pcType″:″DATE″,″precision″:23,″scale″:3,″columnIndex″:4,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:4,″linkedFields″:[ ],″relatedInfos″:[ ],″references″:[]},{″@type″:″field″,″name″:″SAL″,″type″:″decimal″,″uniqueName″:″SAL″,″label″:″SAL″,″parentObject″:″EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:7,″scale″:2,″columnIndex″:5,″isKey″:false,″isExternalId″:false, ″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:5,″linkedFields″:[″salary″],″relatedInfos″:[ ],″references″:[]},{″@type″:″field″,″name″:″COMM″,″type″:″decima1″,″uniqueName″:″COMM″,″label″:″COMM″,″parentObject″:″EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:7,″scale″:2,″columnIndex″:6,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:6,″linkedFields″:[],″relatedInfos″: [ ],″references″:[],{″@type″:″field″,″name″:″DEPTNO″,″Ope″:″decimal″,″uniqueName″:″DEPTNO″,″label″:″DEPTNO″,″parentObject″:″EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:2,″scale″:0,″columnlndex″:7, ″isKey″:false, ″isExternalId″:false,″isSfldLookup ″:false, ″isNullable″:true,″isUnique″.false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:7,″linkedFields″:[],″relatedInfos″:[ ]″references″:[ ]}], ″sortFields″:[ ],″groupFields″:[]}

In one exemplary embodiment, during operation 2134, one or more topicsubscriptions tasks may be generated in accordance with the followingexemplary instructions, which may be instructions of a data integrationtool, e.g., tool 112:

URI: /saas/api/v2/dsstask Request Payload:{″@type″:″dssTask″,″orgId″:″010UQX″,″name″:″empSub″,″description″:″,″createTime″:″2019-09-22T11:45 :58.000Z″,″updateTime″:″2019-11-17T13:08:11.000Z″,″createdBy″:″cihilabs″,″updatedBy″:″cihilabs″,″maxLogs″:10,″sourceConnectionId″:″010UQX0B000000000002″,″targetConnectionId″:″010UQX0B000000000003″,″targetObject″:″EDC_TARGET_EMP″,″targetObjectLabel″:″EDC_TARGET_EMP″,″operation″:″Insert″,″maxRows″:0,″truncateTarget″:false,″bulkApiDBTarget″:false,″verbose″:false,″targetMetadataUpdated″:false,″modelVersion″:″V3-R600″,″queryAll″:false,″srcSettings″:{″@type″:″taskDataSourceSetting″,″isShowLabels″:true,″isNaturalOrder″:true),″tgtSettings″:{@type″:″taskDataSourceSetting″,″isShowLabels″:false,″isNaturalOrder″:true),″cfSettings″:{″@type″:″taskDataSourceSetting″,″isShowLabels″:false,″isNaturalOrder″:false},″bulkApi″:false,″bulkApiSerialMode″:false,″bulkApiMonitor″:false,″isTargetObjectSfCustom″:false,″allowNullUpdates″:false,″targetBatchSize″:200,″assignmentRuleId″:″-1″,″assignmentRuleType″:″None″,″createSuccessFile″:false,″bulkApiHardDelete″:false,″srcRuntimeAttrs″: {″@type″:″taskRuntimeAttrs″,″attrs″: [″Cloud Integration HubBatch interval″:″1000″,″Cloud Integration Hub Subscriptionname″:″empSub″}},″tgtRuntimeAttrs″:{″@type″:″taskRuntimeAttrs″,″attrs″:{}),″sourceObjects″: [{″@type″:″mObject″,″name″: ″employee/emp″,″label″:″employee/emp″,″metadataUpdated″:false, ″relations″:[ ]″children″:[]}],″advancedFilters″: [ ],″filters″:[ ],″fieldMaps″:[{″@type″:″fieldMap″, ″targetField″:{″@type″:″field″,″name″:″EMPNO″,″type″:″decimal″,″uniqueName″:″EMPNO″,″label″:″EMPNO″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:4,″scale″: 0,″columnIndex″:0,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:0,″linkedFields″:[″id″],″relatedInfos″: [ ], ″references″:[]},″expression″:″id″),{″@type″:″fieldMap″,″targetField″: {″@type″:″field″,″name″:″ENAME″,″type″:″varchar″,″uniqueName″:″ENAME″,″label″:″ENAME″,″parentObject″:″EDC_TARGET_EMP″,″pcType″: ″STRING″,″precision″:10,″scale″:0,″columnIndex″:1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabe″:false,″naturalOrder″:1,″linkedFields″.[″name″],″relatedInfos″: [ ],″references″:[]},″expression″:″name″},{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:JOB″,″type″:″varchar″,″uniqueName″: ″JOB″,″label″:″JOB″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″STRING″,″precision″:9,″scale″:0,″columnIndex″:2,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false,″naturalOrder″:2 ,″linkedFields″: [],″relatedInfos″: [ ],″references″: [ ]}},{″@type″:″fieldMap″,″targetField″: {″@type″:″field″,″name″: ″MGR″,″type″:″decimal″,″unique Name″:″MGR″,″label″:″MGR″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:4,″scale″:0,″columnIndex″:3,″isKey″:false,″isExternalId″.false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true ,″isCalculated″:false ,″showLabel:false,″naturalOrder″:3,″linkedFields″: [ ] ,″relatedInfos″ : [],″references″[ ]}}, {″@type″: ″fieldMap″,″targetField″: {″@type″:″field″,″name″:″HIREDATE″,″type″:″timestamp″,″uniqueName″:″HIREDATE″,″label″:″HIREDATE″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″DATE″,″precision″:23,″scale″:3,″columnIndex″:4,″isKey″:false ,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false ,″isCreateable″:false,″isUpdateable″:true ,″isFilterable″:true,″isCalculated″false,″showLabel″:false,″naturalOrder″:4, ″linkedFields″:[ ],″relatedInfos″: [ ],″references″:[]}},{″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″:″SAL″,″type″:″decimal″,″uniqueName″:″SAL″,″label″:″SAL″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″HIPRECDECIMAL″,″precision″: 7, ″scale″:2,″columnIndex″:5,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″showLabel″:false, ″naturalOrder″:5,″linkedFields″: [″salary″],″relatedInfos″: [ ],″references″: []},″expression ″: ″salary″), {″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″: ″COMM″,″type″:″decimal″,″uniqueName″:″COMM″,″label″:″COMM″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″HIPRECDECIMAL″,″precision″: 7,″scale″:2 ,″columnIndex″: 6,″isKey″:false,″isExternalId″:false,″ isSfldLookup″:false,″isNullable″:true,″isUnique″:false ,″isCreateable″:false,″isUpdateable″:true, ″isFilterable″:true ,″isCalculated″:false ,″showLabel″:false ,″naturalOrder″:6,″linkedFields″: [ ],″ relatedInfos″:[],″references″:[ ]}}, {″@type″:″fieldMap″,″targetField″:{″@type″:″field″,″name″: ″DEPTNO″,″type″:″decimal″,″uniqueName″:″DEPTNO″,″label″:″DEPTNO″,″parentObject″:″EDC_TARGET_EMP″,″pcType″:″HIPRECDECIMAL″,″precision″:2,″scale″:0,″columnIndex″:7,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false ,″show Label″:false, ″naturalOrder″:7,″linkedFields″:[ ],″relatedInfos″:[ ],″references″: []}}],″sourceFields″: [{″@type″:″field″,″name″:″id″,″type″:″long″,″uniqueName″:″id″,″label″:″id″,″parentObject″:″employee/emp″,″pcType″:″BIGINT″,″precision″:19,″scale″:1,″columnIndex″:-1,″isKey″:false,″isExternalId″.false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″jlavaType″:″java.1ang.Long″,″showLabel″:true,″naturalOrder″:0,″linkedFields″:[″EMPNO″],″relatedInfos″:[] , ″references″:[ ]},{″@type″:″field″,″name″:″name″,″type″:″string″,″uniqueName″:″name″,″label″:″name″,″parentObject″:″employee/emp″,″pcType″:″NSTRING″,″precision″: 255,″scale″: 0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false ,″isUpdateable″:true ,″isFilterable″:true,″isCalculated″:false ,″javaType″:″java.lang.String″,″showLabel″:true,″naturalOrder″:1,″linkedFields″:[″ENAME″],″related Infos″: [ ],″references″:[ ]},{″@type″:″field″,″name″:″age″,″type″:″decimal″,″uniqueName″:″age″,″label″:″age″,″parentObject″:″employee/emp″,″pcType″:″DECIMAL″,″precision″:3,″scale″:0,″columnIndex″:-1,″isKey″:false ,″isExternalId″:false,″isSfldLookup″:false ,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.math.BigDecimal″,″showLabel″:true,″naturalOrder″:2,″linkedFields″:[ ] , ″relatedInfos″: [ ],″references″:[ ]},{″@type″:″field″,″name″:″city″,″type″:″string″,″uniqueName″:″city″,″label″:″city″,″parentObject″:″employee/emp″,″pcType″:″NSTRING″,″precision″:50,″scale″: 0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false ,″isNullable″:true,″isUnique″:false,″isCreateable″:false ,″isUpdateable″:true,″isFilterable″:true ,″isCalculated″:false,″javaType″:″java.lang.String″,″showLabel″:true,″naturalOrder″:3,″linkedFields″:[ ], ″relatedInfos″:[ ],″references″:[]},{″@type″:″field″,″name″:″salary″,″type″:″decimal″,″uniqueName″:″salary″,″label″:″salary″,″parentObject″:″employee/emp″,″pcType″:″DECIMAL″,″Precision″:15,″scale″: 2,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfidLookup″:false, ″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.math.BigDecimal″,″showLabel″:true,″naturalOrder″:4,″linkedFields″:[″SAL″],″relatedInfos″:[ ],″references″:[]},{″@type″:″field″,″name″:″DIH_ PUBLICATION_INSTANCE_DATE″,″type″:″datetime″,″uniqueName″:″DIH_PUBLICATION_INSTANCE_DATE″,″label″:″DIH_PUBLICATION_INSTANCE_DATE″,″parentObject″:″employee/emp″,″pcType″:″TOOLKIT_DATETIME″,″precision″:26,″scale″:0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.sql.Timestamp″,″showLabel:true,″naturalOrder″:5,″linkedFields″:[],″relatedInfos″:[ ],″references″:[]},{″@type″:″field″,″name″:″DIH_PUBLICATION_INSTANCE_ID″,″type″:″string″,″uniqueName″:″DIH_PUBLICATION_INSTANCE_ID″,″label″:″DIH_PUBLICATION_INSTANCE_ID″,″parentObject″:″employee/emp″,″pcType″:″NSTRING″,″precision″:19,″scale″:0,″columnIndex″:-1,″isKey″:false,″isExternalId″:false,″isSfldLookup″:false,″isNullable″:true,″isUnique″:false,″isCreateable″:false,″isUpdateable″:true,″isFilterable″:true,″isCalculated″:false,″javaType″:″java.lang.String″,″showLabel″:true,″naturalOrder″:6,″linkedFields″:[], ″relatedInfos″:[ ],″references″:[ ]}],″sortFields″:[],″groupFields″:[ ])

FIG. 24 illustrates a computing device 2410 in accordance with thisdisclosure, which includes a processing device 2411, e.g., which mayserve as processor 118, memory 2412, a bus network 2414, an outputcontroller 2415 providing output to an output device 2420, such as adisplay or a printer (not particularly illustrated), a storage device2413, a communications connection 2440, e.g., for wireless or wiredconnectivity, and an input controller 2416 for receiving user input,e.g., via a user device 2430. In some embodiments, computer softwareinstructions are retrieve from storage device 2413 by processing device2411 via bus 2414 and stored in memory 2412, from which processingdevice 2411 executes such instructions causing computing device 2410overall to carry out various techniques in accordance with thisdisclosure. For example, instructions for a pub/sub engine 122 b may beconfigured to, among other things, carry out one or more of processes300, 400, 1500, or 1600, or variations thereof in accordance with thisdisclosure. Upon reading this disclosure in its entirety, one willappreciate how to configure computer software instructions to carry outoperations in accordance with this disclosure using a variety ofcomputer software languages interacting with various data stores andvarious components of a computing device, e.g. computing device 2410that is integrated, e.g. via a network, with a data store, e.g., datastore 102.

We claim:
 1. A method executed by one or more computing devices foroptimizing a plurality of data integration tasks within a dataintegration collection describing data integration tasks within a datastore, the method comprising: accessing, using at least one computerprocessor, a data integration collection describing a plurality of dataintegration tasks defining a migration of data between at least onesource data store and at least one target data store; identifying, usingat least one computer processor, as a sub-set of the plurality of dataintegration tasks, a plurality of point-to-point data integration taskseach corresponding to a respective data source and a respective target;defining, using the at least one computer processor, a data integrationtransformation plan to include: generating one or more publication dataintegration tasks comprising publishing from each respective data sourceof the plurality of point-to-point data integration tasks to generate asingle publication topic; and generating one or more subscription dataintegration tasks causing each respective target of the plurality ofpoint-to-point data integration tasks to subscribe to the singlepublication topic; and generating, using the at least one computerprocessor, a set of optimization instructions configured to cause thedata integration transformation plan.